Soft decision output decoder for decoding convolutionally encoded

Abstract

A soft decision output decoder and decoding method. The decoder decodes a sequence of signals output by an encoder and transmitted over a channel. The soft decision output decoder includes a first 'generalized' Viterbi decoder for decoding the sequence of signals received over the channel during a forward iteration through a trellis representing the encoder output having a block length T. The first 'generalized' Viterbi decoder begins at an initial state t.sub.0 and provides a plurality of forward iteration state metrics .alpha. for each state at each time interval over a window of length 2L, where L is on the order of a few constraint lengths and 2L is less than a block length T. A second 'generalized' Viterbi decoder decodes the sequence of signals received over the channel during a backward iteration through the trellis. The second decoder starts at a second time t2L and provides a plurality of backward iteration state metrics .beta. for each state at each time interval. A processor then performs a dual maxima computation at each state using the forward state metric, the backward state metric and the branch metric for same to provide a measure of the likelihood that a particular sequence of data was transmitted by the encoder. The processor computes a log of the likelihood ratio using the forward and backward state metrics and the branch metrics for a selected state. This is achieved by first computing a max function as an approximation of the measure of the likelihood that a particular sequence of data was transmitted by the encoder. By performing forward and backward Viterbi decoding with dual maxima computations at each node within a window moved over the trellis, the inventive decoder provides the performance benefits associated with a LOG-MAP decoder while avoiding the excessive memory requirements of same.